O-Plan: a Knowledge-Based Planner and its Application to Logistics

O-Plan is a command, planning and control architecture with an open modular structure intended to allow experimentation on, or replacement of, various components. The research is seeking to determine which functions are generally required in a number of application areas and across a number of different comm~nd, p]a,n|ng, scheduling and control systems. O-Plan alms to demonstrate how a planner, situated in a task assignment and plan execution (command and control) environment, and using extensive domain knowledge, can allow for flexible, distributed, collaborative, and mixedinitiative planning. The research is seeking to verify this total systems approach by studying a simplified three-level model with separable task assignment, plan generation and plan execution agents. O-Plan has been applied to logistics tasks that require flexible response in changing situations. Summary The O-Plan research and development project is seeking to identify re-usable modules and interfaces within planning systems which will enable such systems to be tailored or extended quickly to meet new requirements. A common framework for representing and reasoning about plans based on the manipulation of constraints underlies the model used by the architecture. Within this framework, rich models of an application domain can be provided to inform the planner when creating or adapting plans for actual use. A number of important foundations have been laid for flexible planning work in the future. They are: ¯ A view of the planner as situated ill the context of task assignment, plan execution and change. ¯ A simple abstract architecture based on an agenda of "issues" from which items can be selected for processing. The processing takes place on an available computational platform (human or machine), with the appropriate functional capabilities described as knowledge sources. This architecture allows for independent progress to be made in a number of important areas for successful planning systems, including search control and opportunism, planner capability description, and system resource scheduling. ¯ A structure that allows separate (often specialised) handlers for different types of constraint to be inchided, so that the results provide effective overall constraints on the operation of a planner.

[1]  James A. Hendler,et al.  Readings in Planning , 1994 .

[2]  Northrup Fowler,et al.  The ARPA-Rome Knowledge-Based Planning and Scheduling Initiative , 1995, IEEE Expert.

[3]  Austin Tate,et al.  Synthesizing Protection Monitors from Causal Structure , 1994, AIPS.

[4]  Austin Tate,et al.  Representing Plans as a Set of Constraints - the Model , 1996, AIPS.

[5]  Howard Beck TOSCA: a novel approach to the management of job-shop scheduling constraints , 1993 .

[6]  Yolanda Gil,et al.  Domain-Specific Criteria to Direct and Evaluate Planning Systems , 1994 .

[7]  Austin Tate,et al.  The Use of Optimistic and Pessimistic Resource Profiles to Inform Search in an Activity Based Planner , 1994, AIPS.

[8]  G. A. Reece Characterization and design of competent rational execution agents for use in dynamic environments , 1995 .

[9]  Brian Austin Tate Using goal structure to direct search in a problem solver , 1975 .

[10]  Mark Drummond,et al.  Exploiting temporal coherence in nonlinear plan construction , 1988, Comput. Intell..

[11]  Austin Tate,et al.  O-Plan: The open Planning Architecture , 1991, Artif. Intell..

[12]  Steven A. Vere,et al.  Planning in Time: Windows and Durations for Activities and Goals , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Austin Tate,et al.  The Use of Condition Types to Restrict Search in an AI Planner , 1994, AAAI.

[14]  Brian Drabble,et al.  Associating AI Planner Entities with an Underlying Time Point Network , 1991, EWSP.

[15]  Yolanda Gil,et al.  Knowledge Refinement in a Reflective Architecture , 1994, AAAI.

[16]  Austin Tate,et al.  Generating Project Networks , 1977, IJCAI.